The impact of digital pathology on immuno-oncology

Immuno-oncology is a growing field in cancer research with significant impact on personalized therapies aided by digital pathology techniques.
Written by Aiforia

Immuno-oncology (IO) relies on harnessing our own immune systems in order to develop personalized therapies and medications for cancer. For this, a deep understanding of the immune system as well as its role in the regulation of the tumor microenvironment (TME) is pivotal. 

While some immunotherapies aim to enhance overall immunity, more effective treatments target specific cancer cells by teaching the immune system to recognize them as foreign pathogens, a process known as immunosurveillance. The anti-tumoral response is also heavily dependent on tumor capacity to reject the infiltration of immune cells in the TME. 

The “hot” tumors with pronounced immune cell infiltration have been shown to respond better to immunotherapies as compared to “cold” tumors. Thus, there are two major goals of IO:

  1. Enhance the immune system to better recognize the cancer cells as foreign bodies
  2. Understand how to “fire up” the cold tumors

To achieve these goals, a better understanding of the immunosurveillance process and tumor rejection in a spatial context of the TME is needed. Fortunately, detailed analysis and exploration of the TME has advanced significantly due to digital pathology and new tools for deep spatial profiling of tissues. 

Digital Pathology

Pathology has become increasingly branched, creating a subfield with a focus on digital data interpretation and management. Traditional glass slides are scanned to create high-resolution digital images with significant improvements in quality and productivity of a pathologist’s work.

Effective immunosurveillance has many moving parts and manipulating its mechanisms is an immense task. Through comprehensive high-resolution images of tumor tissue, researchers are accessing new details of cancerous cells. Zoom features and multiple angles provide better views of tissue samples and worldwide access to the digital space allows pathologists to specialize in a field, collaborate with colleagues, and outsource data and analyses with browser-based platforms.

A shortage of pathologists and digitized slides have also promoted development of AI-assisted image analysis. Objective algorithms and large storage capacities for predictive analytics are decreasing hours of manual slide reading. Consequently, computers are becoming faster and more accurate than traditional glass slide reads, which are often influenced by cognitive and visual inter- and intra-observer biases. Digital pathology is a gateway to navigate the complexity of the immune system and better understand tumor microenvironments and intercellular interactions.

Tumor Microenvironment

Immuno-oncology requires a deep understanding of TME, including the differentiation of cell types and their spatial contexts. Increasingly detailed images from digitized slides are making way for analysis of previously inaccessible information on intracellular mechanisms. Immunohistochemistry (IHC) is a critical tool for IO as it allows analysis of specific proteins and cell structures within the intact spatial context of the tumor. 

Nevertheless, the number of targets detected by conventional IHC is limited, which has led to the development of multiplexed IHC enabling simultaneous detection of up to hundreds of targets in a single sample and still preserving the original spatial context of the sample. The multiplexed assays are pivotal for accurate and detailed analyses of immune cells and their interaction with the tumor cells. 

Quantification and evaluation of biomarkers and their spatial contexts has significant effects on immunotherapy and shown to improve clinical results. For example, lymphocyte distributions within a TME can predict immunotherapy  efficacy in colorectal cancer.

Artificial intelligence is also promoting quantifiable data on tumor microenvironments for clinical results, such as differentiation of viable and necrotic tissue. This precision and specificity are beneficial for the personalization of treatment through disease tracking and predictions of treatment efficacy.

Cell-Cell Interactions

An important target for current immunotherapies are checkpoint inhibitors (CPI). CPIs regulate whether the immune system reacts to other cells as friends or foes. In tumors, the cancer cells may express the CPI proteins on their surface allowing the cancer cells to evade the immune system’s anti-tumoral response. IHC for the CPIs is an important assay to measure how much the cancer cells express the CPIs and to determine the potential effect of anti-CPI therapy. The CPI-assay is an example of a targeted biomarker assay and can reveal cell-cell interactions within a microenvironment, such as those of immune cells and tumor cells. This may indicate methods to decrease tumor growth and increase cytotoxic T cell function.

Measurement of variation between endogenous and therapy-induced T cell count is a powerful tool for immunotherapy response predictions. A whole slide image of a tissue section can tag the tumor microenvironment and its components, such as tumor cells, invasive margin, and stroma. These results are digitized and followed by analysis of infiltrating T cells to understand which immune cells fight the tumor and its therapeutic benefits.

T-cell receptors are also patient and tumor specific, promoting more personalized therapy. A lot of the data is easily quantifiable, such as counting immune cells within a cancerous area. Quantifiable biomarker data is a reflection of the immune response and reproducible for method validation.

Clinical Context and Conclusion

Clinical development looks at changes in immune cell infiltration and biomarker expression before and after therapeutic intervention as parameters. Immuno-oncology has the benefit of being a lifelong cure as the immune system’s memory can constantly monitor and reduce the return of cancer cells. IO and its digital component are a new era of cancer treatment that goes beyond a one-size-fits-all approach by predicting patient response to immunotherapies.

IO blog post (Image from Darshan)

Immuno-oncology has benefited greatly from technological innovations in healthcare and the use of digital pathology. Image analysis, personalized therapies, and predictable medical responses are expanding the possibilities of cancer research.

However, the opposite is also true whereby IO developments are influencing and improving our perception of the utility of digital pathology. Beginning with navigation of the complexity of the tumor microenvironment and choosing specific patients for IO medical trials, digital pathology will continue to develop in the direction of highest clinical utility rather than merely affect which route immuno-oncology will follow.

 

References

Bowers, Keith. 5 Ways Quantitative Digital Pathology Can Add Value to Immuno-Oncology R&D. oraclebio.com/quantitative-histology-immuno-oncology-blog/

Cisbio. Immuno-oncology and immunotherapy introduction. youtu.be/GRjksD-EcIs

Daniels, G.A. Understanding the Role of Immuno-Oncology in Treating Cancer.

www.cancercare.org/publications/285-understanding_the_role_of_immuno-oncology_in_treating_cancer

Koelzer, V.H., Sirinukunwattana, K., Rittscher, J. et al. Precision immunoprofiling by image analysis and artificial intelligence. doi.org/10.1007/s00428-018-2485-z

Montalto, Mike. An Alternative Image of Digital Pathology. thepathologist.com/subspecialties/an-alternative-image-of-digital-pathology

Ung Christopher, Kockx Mark, Waumans Yannick. Digital pathology in immuno-oncology – a roadmap for clinical development. doi.org/10.1080/23808993.2017.1281737

Williams, Jane. How To Cope With An Evolving Immuno-Oncology Field And Changes In Pathology.

www.global-engage.com/life-science/cope-with-evolving-immuno-oncology-field-changes-pathology/